AI Farming Revolution and Machine Learning Approaches to Government Schemes and Subsidies

Artificial Intelligence made our lives easy. By Emerging in different fields/sectors such as Education, banking, Marketing, Financial and in many E commerce Additionally, farmers can use it to help them obtain more yields & schemes applicable. This article details a web-based tool that helps farmers. , weather forecasting and suitable crops based on the type of soil. Here we can make use of a WEKA software-based tool consisting of different AI & ML techniques. Which makes the job easier to reach every customer & it predicts whether the decision chosen by the farmers regarding crops are Suitable for their farming land. Using a Recommendation System that leverages historical data (demographics, crop type, location, income level) to recommend schemes/subsidies tailored to each farmer’s needs. ML Techniques: Collaborative filtering or content-based filtering can be used to match schemes based on similar farmer profiles. The advent of Artificial Intelligence (AI) has transformed many sectors, which include education, banking, marketing, finance, and e-commerce. Agriculture, this lifeblood of the planet’s sustenance, is being revolutionized by AI-driven inventions as well. This paper looks at the use of AI and Machine Learning to change farming practices and enhance accessibility to government schemes and subsidies. The proposed system brings forth a web-based platform that utilizes AI and ML technologies to help farmers improve crop yield, find the best farming practices, and get government schemes relevant to their needs. The system allows farmers to make informed decisions by combining weather forecasting, soil analysis, and crop selection. The platform uses WEKA, a data mining and ML software, to implement a range of AI and ML techniques that streamline data analysis and predictive modeling. One of the most important aspects of this system is the recommendation engine, which recommends to the farmers personalized suggestions. The recommendation system bases its suggestion on historical data, such as farmer demographics, crop types, location, and income levels, to suggest relevant government schemes and subsidies. Utilizing ML approaches such as collaborative filtering and content-based filtering, the system identifies the patterns and relationships between the farmers who have similar profiles, ensuring accurate and relevant recommendations.